Tony Parker is one of the best playmakers in the world. For more than a decade now, he’s been the straw that stirs the Spurs’ stiff offensive drink. But despite winning three rings and an NBA Finals MVP, Parker has never quite been considered a true superstar. Once again this year, he’ll begin the All-Star Game on the bench, playing behind guards who have somehow turned slighter successes into superior Q scores. Maybe this is because Parker is a foreign player, or maybe it’s because he plays in a smaller market deep in the heart of Texas.

But maybe it’s also because our box scores undervalue the importance of the “little things” that players like Parker do and overvalue the most easily quantifiable events like made baskets and rebounds.

On one hand, the notion that we award Leonard three points for his buzzer-beating shot in Cleveland makes sense. After all, he was the one who made the freaking shot. On the other hand, giving Leonard credit for the basket is like awarding George Clooney the credit for Gravity.

“We practiced that play 1,000 times, so I knew we’d be able to execute it,” San Antonio coach Gregg Popovich said after the game.

If we applied this conventional basketball accounting to the game of chess, we’d assign far too much importance to the singular checkmate move, while entirely overlooking that move’s hugely relevant tactical precedents. Chess matches are rarely won or lost in one final action, and the same goes for basketball possessions. They are rarely decided by their terminal actions, and players like Parker or Chris Paul commonly put their teams in advantageous situations one way or another.

In the era of “big data,” the current statistical system — the one that produces the box score — is a typewriter, albeit a reliable one. It was born out of pencil-and-paper convenience rather than a desire to truly measure the contributions of the 10 athletes on the floor. Still, it has worked well, and as a result it’s persisted from the time of Bill Russell, through the Michael Jordan years, and well into the LeBron James era; its derivative dogmas have morphed into things we have termed “advanced stats” and “basketball analytics.”

In the last few decades, pioneers like Ken Pomeroy, Dean Oliver, and John Hollinger effectively took advantage of spreadsheets and other newfangled accoutrements of the personal computing era to launch us headlong into basketball’s computational era. We continue to learn from their contributions, but things are still rapidly evolving.

I don't have the background knowledge to truly understand this, but it sounds promising. Its biggest flaw seems to be that it doesn't seem to evaluate players well outside the context of their teammates (players who are good at passing to great shooters will do very well, but it's the great shooter who deserves a lot of the credit). Still, if this works, it could revolutionize Basketball in the way Baseball was revolutionized (although it's probably far more difficult to pull off given the speed of the game and the number of interactions that happen at once).